# Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks

arXiv: https://arxiv.org/abs/2106.01862 · HTML: https://mlsanity.com/paper/2106.01862/

## Key facts

- Cited by 100 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 57 references that are in the map.
- First posted on arXiv 3 Jun 2021.

## Builds on (most cited first, 30 of 34)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks](https://mlsanity.com/paper/1706.02609/) (2017)
- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [Deep Residual Learning in Spiking Neural Networks](https://mlsanity.com/paper/2102.04159/) (2021)
- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion](https://mlsanity.com/paper/1812.08156/) (2018)
- [Going Deeper With Directly-Trained Larger Spiking Neural Networks](https://mlsanity.com/paper/2011.05280/) (2020)
- [SLAYER: Spike Layer Error Reassignment in Time](https://mlsanity.com/paper/1810.08646/) (2018)
- [The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://mlsanity.com/paper/1801.10202/) (2018)
- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [SuperSpike: Supervised learning in multi-layer spiking neural networks](https://mlsanity.com/paper/1705.11146/) (2017)
- [Long short-term memory and learning-to-learn in networks of spiking neurons](https://mlsanity.com/paper/1803.09574/) (2018)
- [A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation](https://mlsanity.com/paper/1804.01306/) (2018)
- [Delving Deeper into Convolutional Networks for Learning Video Representations](https://mlsanity.com/paper/1511.06432/) (2015)
- [Event-based Moving Object Detection and Tracking](https://mlsanity.com/paper/1803.04523/) (2018)
- [Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy](https://mlsanity.com/paper/2009.08283/) (2020)
- [Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks](https://mlsanity.com/paper/2103.12593/) (2021)
- [Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks](https://mlsanity.com/paper/2003.06696/) (2020)
- [Focus Is All You Need: Loss Functions For Event-based Vision](https://mlsanity.com/paper/1904.07235/) (2019)
- [Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception](https://mlsanity.com/paper/1807.10936/) (2018)
- [Event-Based Angular Velocity Regression with Spiking Networks](https://mlsanity.com/paper/2003.02790/) (2020)
- [Sparse Spiking Gradient Descent](https://mlsanity.com/paper/2105.08810/) (2021)
- [Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences](https://mlsanity.com/paper/2005.02183/) (2020)
- [Distance Surface for Event-Based Optical Flow](https://mlsanity.com/paper/2003.12680/) (2020)
- [SpikeMS: Deep Spiking Neural Network for Motion Segmentation](https://mlsanity.com/paper/2105.06562/) (2021)

## Built on by (most cited first, 30 of 100)

- [Secrets of Event-Based Optical Flow](https://mlsanity.com/paper/2207.10022/) (2022)
- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [Advancing Spiking Neural Networks towards Deep Residual Learning](https://mlsanity.com/paper/2112.08954/) (2021)
- [Learning Dense and Continuous Optical Flow from an Event Camera](https://mlsanity.com/paper/2211.09078/) (2022)
- [Event-based Video Reconstruction via Potential-assisted Spiking Neural Network](https://mlsanity.com/paper/2201.10943/) (2022)
- [Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow](https://mlsanity.com/paper/2303.05214/) (2023)
- [Event Collapse in Contrast Maximization Frameworks](https://mlsanity.com/paper/2207.04007/) (2022)
- [Optical flow estimation from event-based cameras and spiking neural networks](https://mlsanity.com/paper/2302.06492/) (2023)
- [TMA: Temporal Motion Aggregation for Event-based Optical Flow](https://mlsanity.com/paper/2303.11629/) (2023)
- [Lightweight Event-based Optical Flow Estimation via Iterative Deblurring](https://mlsanity.com/paper/2211.13726/) (2022)
- [Direct Learning-Based Deep Spiking Neural Networks: A Review](https://mlsanity.com/paper/2305.19725/) (2023)
- [Fully neuromorphic vision and control for autonomous drone flight](https://mlsanity.com/paper/2303.08778/) (2023)
- [Real-Time Optical Flow for Vehicular Perception with Low- and High-Resolution Event Cameras](https://mlsanity.com/paper/2112.10591/) (2021)
- [Learning Optical Flow from Event Camera with Rendered Dataset](https://mlsanity.com/paper/2303.11011/) (2023)
- [Motion-prior Contrast Maximization for Dense Continuous-Time Motion Estimation](https://mlsanity.com/paper/2407.10802/) (2024)
- [StereoSpike: Depth Learning with a Spiking Neural Network](https://mlsanity.com/paper/2109.13751/) (2021)
- [Multi-Event-Camera Depth Estimation and Outlier Rejection by Refocused Events Fusion](https://mlsanity.com/paper/2207.10494/) (2022)
- [Emergent Visual Sensors for Autonomous Vehicles](https://mlsanity.com/paper/2205.09383/) (2022)
- [Adaptive-SpikeNet: Event-based Optical Flow Estimation using Spiking Neural Networks with Learnable Neuronal Dynamics](https://mlsanity.com/paper/2209.11741/) (2022)
- [EvHandPose: Event-based 3D Hand Pose Estimation with Sparse Supervision](https://mlsanity.com/paper/2303.02862/) (2023)
- [Microsaccade-inspired Event Camera for Robotics](https://mlsanity.com/paper/2405.17769/) (2024)
- [A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework](https://mlsanity.com/paper/2212.07350/) (2022)
- [Enhancing Traffic Object Detection in Variable Illumination with RGB-Event Fusion](https://mlsanity.com/paper/2311.00436/) (2023)
- [Event-based Temporally Dense Optical Flow Estimation with Sequential Learning](https://mlsanity.com/paper/2210.01244/) (2022)
- [Towards Anytime Optical Flow Estimation with Event Cameras](https://mlsanity.com/paper/2307.05033/) (2023)
- [Deblurring Neural Radiance Fields with Event-driven Bundle Adjustment](https://mlsanity.com/paper/2406.14360/) (2024)
- [Fast Event-based Optical Flow Estimation by Triplet Matching](https://mlsanity.com/paper/2212.12218/) (2022)
- [Neuromorphic Optical Flow and Real-time Implementation with Event Cameras](https://mlsanity.com/paper/2304.07139/) (2023)
- [Event-based Background-Oriented Schlieren](https://mlsanity.com/paper/2311.00434/) (2023)
- [Highly Efficient 3D Human Pose Tracking from Events with Spiking Spatiotemporal Transformer](https://mlsanity.com/paper/2303.09681/) (2023)

## Abstract (arXiv)

The field of neuromorphic computing promises extremely low-power and low-latency sensing and processing. Challenges in transferring learning algorithms from traditional artificial neural networks (ANNs) to spiking neural networks (SNNs) have so far prevented their application to large-scale, complex regression tasks. Furthermore, realizing a truly asynchronous and fully neuromorphic pipeline that maximally attains the abovementioned benefits involves rethinking the way in which this pipeline takes in and accumulates information. In the case of perception, spikes would be passed as-is and one-by-one between an event camera and an SNN, meaning all temporal integration of information must happen inside the network. In this article, we tackle these two problems. We focus on the complex task of learning to estimate optical flow from event-based camera inputs in a self-supervised manner, and modify the state-of-the-art ANN training pipeline to encode minimal temporal information in its inputs. Moreover, we reformulate the self-supervised loss function for event-based optical flow to improve its convexity. We perform experiments with various types of recurrent ANNs and SNNs using the proposed pipeline. Concerning SNNs, we investigate the effects of elements such as parameter initialization and optimization, surrogate gradient shape, and adaptive neuronal mechanisms. We find that initialization and surrogate gradient width play a crucial part in enabling learning with sparse inputs, while the inclusion of adaptivity and learnable neuronal parameters can improve performance. We show that the performance of the proposed ANNs and SNNs are on par with that of the current state-of-the-art ANNs trained in a self-supervised manner.